Containment strategies and statistical measures for the control of Bovine Viral Diarrhea spread in livestock trade networks

Abstract: Assessing the risk of epidemic spread on networks and developing strategies for its containment is of tremendous practical importance, both due to direct effects in public health and its impact on economies. In this work we present the numerical results of a stochastic, event-driven, hierarchical agent-based model designed to reproduce the infectious dynamics of the cattle disease called Bovine Viral Diarrhea (BVD), for which the corresponding network of movements is the main route of spreading. For the farm-node dynamics, the model takes into account a vast number of breeding, infectious and animal movement mechanisms via a susceptible-infected-recovered (SIR) type of dynamics with an additional permanently infectious class. The interaction between the farms is described by a supply and demand farm manager mechanism governing the network structure and dynamics. We discuss the disease and breeding dynamics, study numerous mitigation strategies of present and past government regulations taking Germany as a case-study country and perform a sensitivity analysis on key parameters. We argue that the model, subject to calibration, has universal predictive potential, can be extended to diseases beyond BVD and demonstrate that appropriate measures can indeed lead to eradication regarding BVD. We further present the results of the time-series of the model and conduct a statistical analysis of and among the different mitigation strategies.